Individual skill
monte-carlo-methods
Monte Carlo simulation done well — sampling, variance reduction, MCMC diagnostics, and honest uncertainty quantification.
skills/monte-carlo-methods/SKILL.md
Source commit faf37c0859ebf627bfcb5225e8057d088691e0ae
What was checked
- Matches the observed repository HEAD
- Yes
- Scanner rules are current
- No
- Repository scan complete
- No
- Skill scan complete
- No
- Observed behavior
- Not checked yet
- Last checked
- Nov 8, 2026
These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.
Install this skill
Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.
An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.
Repository context
- Repository stars (not skill ratings)
- 54
- Repository last updated
- Oct 10, 2026
- License
- MIT
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- agent-skills Repository topic
- ai-agents Repository topic
- ai-agents-automation Repository topic
- ai-skills Repository topic
- awesome Repository topic
- awesome-list Repository topic
- chatgpt Repository topic
- claude-skills Repository topic
- meta-ai Repository topic
- muse-ai Repository topic
- engineering Derived category
Files and excerpt
File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.
Read excerpt
## Overview
Monte Carlo methods solve integration, optimization, and inference
problems by random sampling — converging as 1/√N regardless of
dimension, which makes them the tool of last resort that usually works.
This skill covers direct sampling, variance-reduction techniques,
Markov chain Monte Carlo (Metropolis–Hastings, HMC/NUTS), and the
convergence diagnostics that separate real posteriors from random walks.
## When to use
- Estimating high-dimensional integrals or expectations
- Propagating uncertainty through a nonlinear model
- Bayesian inference: sampling posteriors with MCMC